doma — not yet audited

by dom-actions · Agent Tool · ★ 137

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About doma

中文 | EN A browser agent from Stay Browser. DomA /ˈdoʊmə/ is an open-source AI web automation agent that runs in the browser. It helps you browse, fill forms, click, collect, and handle other web tasks. With DomA installed, you don't need a dedicated "AI browser" — any extension-capable browser becomes your AI browser. [!TIP] Besides this open-source edition, you can also find the commercial build in the Chrome / Edge stores (DomA for Chrome, DomA for Edge). T

agentsai-agentsbrowser-agentbrowser-usedomaextension-chromeextension-edgejevopen-sourcewebautomation

Quick Facts

Stars137
Forks21
LanguageTypeScript
CategoryAgent Tool
Quality Score62.8816217822038/100
Open Issues4
Last Updated2026-09-24
Created2026-09-09
Platformsbrowser, node
Est. Tokens~14k

doma alternative? Top 6 similar tools

Looking for a doma alternative? If you're comparing doma with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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Frequently Asked Questions

What is doma?

doma is The open-source of DomA, an AI automation agent for the browser.. It is categorized as a Agent Tool with 137 GitHub stars.

What programming language is doma written in?

doma is primarily written in TypeScript. It covers topics such as agents, ai-agents, browser-agent.

How do I install or use doma?

You can find installation instructions and usage details in the doma GitHub repository at github.com/dom-actions/doma. The project has 137 stars and 21 forks, indicating an active community.

What are the best alternatives to doma?

The top alternatives to doma on Agent Skills Hub include socai, gate22, web-agent-protocol. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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